Human Physical Activity Recognition Research Articles

Motion Signal-based Recognition of Human Activity from Video Stream Dataset Using Deep Learning Approach

Background: Human physical activity recognition is challenging in various research eras, such as healthcare, surveillance, senior monitoring, athletics, and rehabilitation. The use of various sensors has attracted outstanding research attention due to the implementation of machine learning and deep learning approaches. Aim: This paper proposes a unique deep learning framework based on motion signals to recognize human activity to handle these constraints and challenges through deep learning (e.g., Enhance CNN, LR, RF, DT, KNN, and SVM) approaches. Method: This research article uses the BML (Biological Motion Library) dataset gathered from thirty volunteers with four various activities to analyze the performance metrics. It compares the evaluated results with existing results, which are found by machine learning and deep learning methods to identify human activity. Result: This framework was successfully investigated with the help of laboratory metrics with convolutional neural networks (CNN) and achieved 89.0% accuracy compared to machine learning methods. Conclusion: The novel work of this research is to increase classification accuracy with a lower error rate and faster execution. Moreover, it introduces a novel approach to human activity recognition in the BML dataset using the CNN with Adam optimizer approach.

PAR-Net: An Enhanced Dual-Stream CNN-ESN Architecture for Human Physical Activity Recognition.

Physical exercise affects many facets of life, including mental health, social interaction, physical fitness, and illness prevention, among many others. Therefore, several AI-driven techniques have been developed in the literature to recognize human physical activities. However, these techniques fail to adequately learn the temporal and spatial features of the data patterns. Additionally, these techniques are unable to fully comprehend complex activity patterns over different periods, emphasizing the need for enhanced architectures to further increase accuracy by learning spatiotemporal dependencies in the data individually. Therefore, in this work, we develop an attention-enhanced dual-stream network (PAR-Net) for physical activity recognition with the ability to extract both spatial and temporal features simultaneously. The PAR-Net integrates convolutional neural networks (CNNs) and echo state networks (ESNs), followed by a self-attention mechanism for optimal feature selection. The dual-stream feature extraction mechanism enables the PAR-Net to learn spatiotemporal dependencies from actual data. Furthermore, the incorporation of a self-attention mechanism makes a substantial contribution by facilitating targeted attention on significant features, hence enhancing the identification of nuanced activity patterns. The PAR-Net was evaluated on two benchmark physical activity recognition datasets and achieved higher performance by surpassing the baselines comparatively. Additionally, a thorough ablation study was conducted to determine the best optimal model for human physical activity recognition.

Stochastic Recognition of Human Physical Activities via Augmented Feature Descriptors and Random Forest Model.

Human physical activity recognition from inertial sensors is shown to be a successful approach for monitoring elderly individuals and children in indoor and outdoor environments. As a result, researchers have shown significant interest in developing state-of-the-art machine learning methods capable of utilizing inertial sensor data and providing key decision support in different scenarios. This paper analyzes data-driven techniques for recognizing human daily living activities. Therefore, to improve the recognition and classification of human physical activities (for example, walking, drinking, and running), we introduced a model that integrates data preprocessing methods (such as denoising) along with major domain features (such as time, frequency, wavelet, and time–frequency features). Following that, stochastic gradient descent (SGD) is used to improve the performance of the extracted features. The selected features are catered to the random forest classifier to detect and monitor human physical activities. Additionally, the proposed HPAR system was evaluated on five benchmark datasets, namely the IM-WSHA, PAMAP-2, UCI HAR, MobiAct, and MOTIONSENSE databases. The experimental results show that the HPAR system outperformed the present state-of-the-art methods with recognition rates of 90.18%, 91.25%, 91.83%, 90.46%, and 92.16% from the IM-WSHA, PAMAP-2, UCI HAR, MobiAct, and MOTIONSENSE datasets, respectively. The proposed HPAR model has potential applications in healthcare, gaming, smart homes, security, and surveillance.

Wearable Internet-of-Things platform for human activity recognition and health care

We propose to perform wearable sensors-based human physical activity recognition. This is further extended to an Internet-of-Things (IoT) platform which is based on a web-based application that integrates wearable sensors, smartphones, and activity recognition. To this end, a smartphone collects the data from wearable sensors and sends it to the server for processing and recognition of the physical activity. We collect a novel data set of 13 physical activities performed both indoor and outdoor. The participants are from both the genders where their number per activity varies. During these activities, the wearable sensors measure various body parameters via accelerometers, gyroscope, magnetometers, pressure, and temperature. These measurements and their statistical are then represented in features vectors that used to train and test supervised machine learning algorithms (classifiers) for activity recognition. On the given data set, we evaluate a number of widely known classifiers such random forests, support vector machine, and many others using the WEKA machine learning suite. Using the default settings of these classifiers in WEKA, we attain the highest overall classification accuracy of 90%. Consequently, such a recognition rate is encouraging, reliable, and effective to be used in the proposed platform.

Data Analysis and Classification of mHealth Shimmer2 Sensor Data sets for Human Physical Activities Recognition

Human physical activity recognition is one of the important attributes for well being of human health and activity recogni-tion field. A physical signal with respect to time based directed toward recognizes human activity events is projected, in the view of this work usage wearable accelerometer sens-ing devices were implanted taking place the human body lo-cation on the upper body Chest Sensor (CS), Left Ankle Sen-sor (LAS) and Right Lower Arms Sensor (RLAS). Accelerome-ter feature extracted based acceleration signals with respect to time, physical appearance of the accelerometer x, y, and z dimension values reported/recorded using shimmer2 weara-ble sensor device is recommended at the categorization of the 10 different users was performed 12 different types human activities, including vigorous and moderate activities. User ages between 24 to 29 years and human body weight (HBW) are 53 to 83 Kg=m2. Results were on view a large validity per-formance precision and recall were getting 95 for each human activities. The whole classifiers accuracy results for all combi-nation of the feature set of all sensors is 98%. The considered work could be used to observe the human body motion of different body location of users and to perform data analysis and classification.

Human Physical Activity Recognition Using Smartphone Sensors.

Because the number of elderly people is predicted to increase quickly in the upcoming years, “aging in place” (which refers to living at home regardless of age and other factors) is becoming an important topic in the area of ambient assisted living. Therefore, in this paper, we propose a human physical activity recognition system based on data collected from smartphone sensors. The proposed approach implies developing a classifier using three sensors available on a smartphone: accelerometer, gyroscope, and gravity sensor. We have chosen to implement our solution on mobile phones because they are ubiquitous and do not require the subjects to carry additional sensors that might impede their activities. For our proposal, we target walking, running, sitting, standing, ascending, and descending stairs. We evaluate the solution against two datasets (an internal one collected by us and an external one) with great effect. Results show good accuracy for recognizing all six activities, with especially good results obtained for walking, running, sitting, and standing. The system is fully implemented on a mobile device as an Android application.

TriboMotion: A Self-Powered Triboelectric Motion Sensor in Wearable Internet of Things for Human Activity Recognition and Energy Harvesting

Human physical activity recognition is widely used in medical diagnosis, well-being management, and rehabilitation treatment. In spite of various Internet of Things (IoT) designs available in the literature, power resources often limit the lifetime of IoT. Regarding this weakness, this paper develops a new motion sensor system in wearable IoT (WIoT) for human physical activity recognition without any signal conditioning circuits. The triboelectricity-based physical model is explored in designing the motion sensor. It enables to collect motion signals caused by physical activities without any power supply. In addition, the triboelectric structure can be used as an energy harvester for motion harvesting due to its high output voltage in random low-frequency motion and a relatively stable voltage when involving continuous activities. Such a new design lays the foundations for constructing the next generation self-powered WIoT systems. Our new design has been extensively evaluated, where most common activities including sitting and standing, walking, climbing upstairs and downstairs, and running are used. The experimental results demonstrate that our system can achieve similar comparable performance as the state of the art for physical activity recognition at an average successful accuracy of over 80%. At the same time, our system reduces more than 25% energy consumption of the entire sensing hardware system which includes the sensor, microcontroller, and corresponding circuits.

A Random Forest-based ensemble method for activity recognition.

This paper presents a multi-sensor ensemble approach to human physical activity (PA) recognition, using random forest. We designed an ensemble learning algorithm, which integrates several independent Random Forest classifiers based on different sensor feature sets to build a more stable, more accurate and faster classifier for human activity recognition. To evaluate the algorithm, PA data collected from the PAMAP (Physical Activity Monitoring for Aging People), which is a standard, publicly available database, was utilized to train and test. The experimental results show that the algorithm is able to correctly recognize 19 PA types with an accuracy of 93.44%, while the training is faster than others. The ensemble classifier system based on the RF (Random Forest) algorithm can achieve high recognition accuracy and fast calculation.

Human activity recognition using quasiperiodic time series collected from a single tri-axial accelerometer

The current generation of portable mobile devices incorporates various types of sensors that open up new areas for the analysis of human behavior. In this paper, we propose a method for human physical activity recognition using time series, collected from a single tri-axial accelerometer of a smartphone. Primarily, the method solves a problem of online time series segmentation, assuming that each meaningful segment corresponds to one fundamental period of motion. To extract the fundamental period we construct the phase trajectory matrix, applying the technique of principal component analysis. The obtained segments refer to various types of human physical activity. To recognize these activities we use the k-nearest neighbor algorithm and neural network as an alternative. We verify the accuracy of the proposed algorithms by testing them on the WISDM dataset of labeled accelerometer time series from thirteen users. The results show that our method achieves high precision, ensuring nearly 96 % recognition accuracy when using the bunch of segmentation and k-nearest neighbor algorithms.

Daily Human Physical Activity Recognition Based on Kernel Discriminant Analysis and Extreme Learning Machine

Wearable sensor based human physical activity recognition has extensive applications in many fields such as physical training and health care. This paper will be focused on the development of highly efficient approach for daily human activity recognition by a triaxial accelerometer. In the proposed approach, a number of features, including the tilt angle, the signal magnitude area (SMA), and the wavelet energy, are extracted from the raw measurement signal via the time domain, the frequency domain, and the time-frequency domain analysis. A nonlinear kernel discriminant analysis (KDA) scheme is introduced to enhance the discrimination between different activities. Extreme learning machine (ELM) is proposed as a novel activity recognition algorithm. Experimental results show that the proposed KDA based ELM classifier can achieve superior recognition performance with higher accuracy and faster learning speed than the back-propagation (BP) and the support vector machine (SVM) algorithms.